Depth estimation is a fundamental computer vision task that aims to use different cues such as color, semantics, stereo, or the difference in image sequences to predict or fuse depth maps [1]}, [2]}.
Most of the previous works assume that input images are all-in-focus, whereas in real-world scenarios,
images are usually considered to be defocused in the background or with a shallow depth-of-field (DoF).
Nevertheless, some approaches elaborate on depth estimation with defocused images. Conventional optimization based approaches [3]}, [4]} proposed to directly estimate depth from focal stacks, and a variant approach [5]} tries to generate an index map in which every pixel is assigned to the focus position leading to the maximal sharpness.
Chen et al. [6]} found the relationship between relative blur and disparity, and make use of it to enhance the robustness of matching.
Depth from focus sweep video [7]} targets estimating depth from images with successive focus positions.
Recently, deep learning based approaches [8]}, [9]} could model the blurriness more precisely and achieve much better depth quality.
On the other hand, some works [10]}, [11]}, [12]} use deep learning techniques to remove the defocus blur for single images.
